U.S. patent number 10,455,080 [Application Number 15/539,101] was granted by the patent office on 2019-10-22 for methods and devices for improvements relating to voice quality estimation.
This patent grant is currently assigned to Dolby Laboratories Licensing Corporation. The grantee listed for this patent is Dolby Laboratories Licensing Corporation. Invention is credited to Shen Huang, Doh-Suk Kim.
United States Patent |
10,455,080 |
Kim , et al. |
October 22, 2019 |
Methods and devices for improvements relating to voice quality
estimation
Abstract
This disclosure falls into the field of voice communication
systems, more specifically it is related to the field of voice
quality estimation in a packet based voice communication system. In
particular the disclosure provides a method and device for 5
reducing a prediction error of the voice quality estimation by
considering the content of lost packets. Furthermore, this
disclosure provides a method and device which uses a voice quality
estimating algorithm to calculate the voice quality estimate based
on an input which is switchable between a first and a second input
mode.
Inventors: |
Kim; Doh-Suk (Cupertino,
CA), Huang; Shen (Beijing, CN) |
Applicant: |
Name |
City |
State |
Country |
Type |
Dolby Laboratories Licensing Corporation |
San Francisco |
CA |
US |
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Assignee: |
Dolby Laboratories Licensing
Corporation (San Francisco, CA)
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Family
ID: |
56151570 |
Appl.
No.: |
15/539,101 |
Filed: |
December 23, 2015 |
PCT
Filed: |
December 23, 2015 |
PCT No.: |
PCT/IB2015/059962 |
371(c)(1),(2),(4) Date: |
June 22, 2017 |
PCT
Pub. No.: |
WO2016/103222 |
PCT
Pub. Date: |
June 30, 2016 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20180013879 A1 |
Jan 11, 2018 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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62128382 |
Mar 4, 2015 |
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Foreign Application Priority Data
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Dec 23, 2014 [WO] |
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PCT/CN2014/94673 |
Jan 27, 2015 [EP] |
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15152715 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L
43/04 (20130101); H04M 3/2236 (20130101); H04L
43/0829 (20130101); G10L 25/60 (20130101) |
Current International
Class: |
H04L
12/26 (20060101); H04M 3/22 (20060101); G10L
25/60 (20130101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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2006/035269 |
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Apr 2006 |
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WO |
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2014/004708 |
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Jan 2014 |
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WO |
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Other References
Ding L. et al., "Non-intrusive single-ended speech quality
assessment in VoIP", Speech Communication, Elsevier Science
Publishers, Amsterdam NL, vol. 49, No. 6, pp. 477-489 XP022113625,
Jun. 1, 2007. cited by applicant .
Jemura S. et al., "Objective speech quality assessment based on
payload discrimination of lost packets for cellular phones in NGN
environment", IEICE Transactions on Communications, Communications
Society, Tokyo JP, vol. E91-B, No. 11, pp. 3667-3676, XP001519001,
Nov. 1, 2008. cited by applicant .
Sofiene J. et al., "Voicing-aware parametric speech quality models
over VoIP networks", Information Infrastructure Symposium, 2009,
GIIS '09, IEEE, Piscataway, NJ, XP031558369, Jun. 23, 2009, pp.
1-8. cited by applicant .
Lingfen S. et al., "Voice quality prediction models and their
application in VoIP networks", IEEE Transactions on Multimedia,
vol. 8 No. 4, pp. 809-820, XP055208109, Aug. 1, 2006. cited by
applicant .
Falk T., "Blind Estimation of Perceptual Quality for Modern Speech
Communications", Canadian theses, XP055208123, Dec. 22, 2008,pp.
1-212. cited by applicant .
Leman A. et al., "Hybrid Model for Non-Intrusive Speech Quality
Evaluation in Telephony Applications", Conference: 38th
International Conference: Sound quality evaluation , AES, New York,
XP040567008, Jun. 13, 2010. cited by applicant .
Nishikawa K. et al., "Extension of Image Transport Protocol
Allowing Sever-Side Control of Request for Retransmission", IEICE
Transactions on Fundamentals of Electronics Communications and
Computer Sciences, Engineering Sciences Society, Tokyo JP, vol.
E87-A, No. 3, pp. 674-681, XP001190007, Mar. 1, 2004. cited by
applicant.
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Primary Examiner: Nguyen; Minh Trang T
Claims
What is claimed is:
1. A method for modifying a statistical metric relating to lost
voice packets in a packet based voice communication system,
comprising the steps of: receiving data comprising a sequence of
encoded voice packets transmitted from one or more end-points in
the voice communication system, wherein encoded voice packets
transmitted from the one or more end-points comprises the received
sequence of encoded voice packets and one or more lost voice
packets which were lost during the transmission from the one or
more end-points or discarded due to latency and/or jitter in the
transmission, the data comprising a signal indicating a perceptual
importance of each of the encoded voice packets transmitted from
the one or more end-points; calculating, based on the received
sequence of encoded voice packets, a statistical metric relating to
the lost voice packets; and modifying the statistical metric based
on the perceptual importance of the lost voice packets, so as to
reduce a prediction error of a voice quality estimate when using
the modified statistical metric, in place of the statistical
metric, as input to a voice quality estimating algorithm configured
to receive and base its estimating on the statistical metric.
2. The method according to claim 1, wherein the statistical metric
includes at least one of: a packet loss rate, PLR, which is the
number of lost voice packets in relation to a total number of
transmitted voice packets, and a burstiness factor, BF, which is
one minus a number of groups of consecutive lost voice packets in
relation to the number of lost voice packets.
3. The method according to claim 2, wherein the step of modifying
the statistical metric based on a perceptual importance of the lost
voice packets comprises linear or non-linear mapping of the PLR to
a perceptual PLR, linear or non-linear mapping of the BF to a
perceptual BF, or both.
4. The method according to claim 1, wherein the step of modifying
the statistical metric comprises weighting the lost voice packets
according to their perceptual importance.
5. The method according to claim 1, wherein the statistical metric
relates to groups of consecutive lost voice packets, each group
comprising one or more lost voice packets, wherein in the step of
calculating the statistical metrics, each group of consecutive lost
voice packets is weighted based on the number of consecutive lost
voice packets in the group, and wherein, in the step of modifying
the statistical metric, each group is further weighted based on the
perceptual importance of the lost voice packets in the group.
6. The method according to claim 1, wherein the perceptual
importance for a lost voice packet is estimated based on perceptual
importance of voice packets in the sequence of encoded voice
packets which are adjacent to the packets that were lost during the
transmission from the one or more end-points or discarded due to
latency and/or jitter of the transmission.
7. The method according to claim 1, wherein each voice packet in
the received sequence of encoded voice packets comprises a separate
bit indicating the perceptual importance of the voice packet.
8. The method according to claim 1, wherein the signal indicating
the perceptual importance includes a respective bit in each voice
packet in the received sequence of encoded voice packets.
9. The method according to claim 1, further comprising the step of:
partially decoding at least some of the received encoded voice
packets in order to estimate perceptual importance of the lost
voice packets.
10. A non-transitory computer-readable storage medium with
instructions adapted to carry out the method of claim 1 when
executed by a device having processing capability.
11. A non-transitory computer-readable storage medium with
instructions adapted to carry out the method of claim 1 when
executed by a device having processing capability.
12. A device for modifying a statistical metric relating to lost
voice packets in a packet based voice communication system, the
device comprising: a receiving stage configured to receive data
comprising a sequence of encoded voice packets transmitted from one
or more end-points in the voice communication system, wherein
encoded voice packets transmitted from the one or more end-points
comprises the received sequence of encoded voice packets and one or
more lost voice packets which were lost during the transmission
from the one or more end-points or discarded due to latency and/or
jitter in the transmission, the data comprising a signal indicating
a perceptual importance of each of the encoded voice packets
transmitted from the one or more end-points; a calculating stage
configured to calculate, based on the received sequence of encoded
voice packets, a statistical metric relating to a number of lost
voice packets; and a perceptual transformation stage configured to
modify the statistical metric based on the perceptual importance of
the lost voice packets, so as to reduce a prediction error of a
voice quality estimate when using the modified statistical metric
as input to a voice quality estimating algorithm.
Description
TECHNICAL FIELD
This disclosure falls into the field of voice communication
systems, more specifically it is related to the field of voice
quality estimation in a packet based voice communication system. In
particular the disclosure provides a method and device for reducing
a prediction error of the voice quality estimation by considering
the content of lost voice packets. Furthermore, this disclosure
provides a method and device which uses a voice quality estimating
algorithm to calculate the voice quality estimate based on an input
which is switchable between a first and a second input mode.
BACKGROUND ART
In previous years, Voice over internet protocol (VoIP) has become
an important application and is expected to carry more and more
voice traffic over TCP/IP networks.
In such Internet protocol (IP)-based voice communications systems,
typically a voice waveform of a user is sliced in time, compressed
by a voice coder, packetized, and transmitted to other users. Due
to the inherent nature of IP networks and real-time constraint of
human voice communications, it is common to lose voice packets
during transmission or that late voice packets are discarded even
if they are received, resulting in degraded voice quality. Mobile
and WIFI networks usually make the situation worse in many cases.
Thus, accurate real-time monitoring of voice quality is an
essential feature for analysis, management, and optimization of
voice communication systems.
A typical voice quality monitoring system adopts a scheme that
analyzes packet loss information, such as packet loss rate and loss
patterns (e.g., if the losses are random or of a bursty nature), as
it provides a simple and computationally inexpensive way to
estimate voice quality. This scheme is known as a modified E-model.
However, these systems suffer from low accuracy in estimating voice
quality since they do not take the content (e.g. the payload) of
the lost voice packets into consideration when estimating the voice
quality.
More accurate voice quality estimation may be achieved by analyzing
voice waveforms after fully decoded all the packets and other data
sent in the VoIP call (e.g. ITU-T P.563, ANSI ANIQUE+). However,
this approach requires extensive computation for analyzing the
voice waveforms. Moreover, this approach discards important packet
loss statistic information available at packet level.
Thus, it is desirable to have a voice quality monitoring system
utilizing both packet loss information and speech waveform
information without an expensive full decoding process.
BRIEF DESCRIPTION OF THE DRAWINGS
Example embodiments will now be described with reference to the
accompanying drawings, on which:
FIG. 1 is a generalized block diagram of a voice quality estimating
device in accordance with an example embodiment,
FIG. 2 shows by way of example a modification of a statistical
metric relating to groups of consecutive lost voice packets,
wherein the modification is based on a perceptual importance of the
lost voice packets in each group,
FIG. 3 describe by way of example how perceptual importance of lost
voice packet is estimated based on voice packets which are adjacent
to the lost voice packets,
FIG. 4 shows by way of example how a prediction error of a voice
quality estimate is reduced when using the perceptual importance of
the lost voice packets when the voice quality estimate is
calculated,
FIG. 5 shows by way of example a method for modifying a statistical
metric relating to lost voice packets,
FIG. 6 shows by way of example a method for calculating a voice
quality estimate.
All the figures are schematic and generally only show parts which
are necessary in order to elucidate the disclosure, whereas other
parts may be omitted or merely suggested. Unless otherwise
indicated, like reference numerals refer to like parts in different
figures.
DETAILED DESCRIPTION
In view of the above it is an object to provide a device and
associated methods which provide a reduced prediction error of a
voice quality estimate by modifying conventional statistical
metrics relating to lost voice packet based on a perceptual
importance of the lost voice packets. Moreover, it is an objective
to provide a device and associated methods which facilitate two
operational modes when calculating a voice quality estimate, such
that a low complexity mode and a high accuracy mode are
provided.
I. Overview--Using Perceptual Importance of Lost Packets
According to a first aspect, example embodiments propose methods
for modifying a statistical metric relating to lost voice packets,
devices implementing the methods, and computer program product
adapted to carry out the method. The proposed methods, devices and
computer program products may generally have the same features and
advantages.
According to example embodiments there is provided a method for
modifying a statistical metric relating to lost voice packets in a
packet based voice communication system.
The method comprises receiving data comprising a sequence of
encoded voice packets transmitted from one or more end-points in
the voice communication system, wherein encoded voice packets
transmitted from the one or more end-points comprises the received
sequence of encoded voice packets and one or more lost voice
packets which were lost during the transmission from the one or
more end-points or discarded due to latency and/or jitter in the
transmission.
The method further comprises calculating, based on the received
sequence of encoded voice packets, a statistical metric relating to
the lost voice packets, and modifying the statistical metric based
on a perceptual importance of the lost voice packets, so as to
reduce a prediction error of a voice quality estimate when using
the modified statistical metric as input to a voice quality
estimating algorithm.
This disclosure relates generally to a teleconferencing system
comprising a plurality of telephone endpoints, and in particular to
the improvement of perceived call quality when such a system
experiences channel degradation or network degradation.
By way of background, in a typical teleconferencing system, a mixer
receives a respective uplink data stream from each of the telephone
endpoints in a voice call, which carries an audio signal captured
by that telephone endpoint, and sends a respective downlink data
stream to each of the telephone endpoints. Consequently, each
telephone endpoint receives a downlink data stream which carries a
mixture of the respective audio signals captured by the other
telephone endpoints. Accordingly, when two or more participants in
a telephone conference speak at the same time, the other
participant(s) can hear both participants speaking.
If there is a problem with the data channel which carries the
downlink and uplink data streams to and from one of the endpoints,
this may cause errors in the downlink and/or uplink data streams.
The errors may be perceptible to the participant using said one of
the endpoints, and/or to other participants in the voice call. The
errors may result in lost voice packets which were lost during the
transmission from the one or more end-points.
The errors may further result in jitter. Jitter is technically the
measure of the variability over time of the latency across a
network and is a common problem in a packet based voice
communication system. Since the voice packets can travel by a
different path from the sender to the receiver, the voice packets
may arrive at their intended destination in a different order then
they were originally sent. Even if a Jitter buffer is used to
temporarily store arriving voice packets in order to minimize delay
variations, some jitter characteristics exceed the capability of a
jitter buffer and some voice packets may still be arriving to late.
These packets are eventually discarded. This discarded voice
packets are thus looked upon as lost voice packets which were
discarded due to latency and/or jitter in the transmission.
The voice packets which are lost may thus be passively lost in
transmission or actively discarded (e.g. by a Jitter buffer) due to
jitter/latency.
This above discussed problems relating to the errors in the
downlink and/or uplink data streams may result in that out of the
encoded voice packets that are transmitted from the one or more
end-points, some are lost during the transmission or discarded due
to latency and/or jitter in the transmission, and the rest are
received as a sequence of encoded voice packets.
As used herein an end-point refers to a telephone endpoint and/or a
mixer. It should be noted that the term telephone endpoint
comprises any endpoint device which can be used in a
teleconferencing system in which sound is converted into electrical
impulses for transmission, and in which electrical impulses are
converted back to sound.
The above method provides a simple and flexible way of reducing the
prediction error of a voice quality estimate.
Conventional statistical metrics when calculating a voice quality
estimate does not take into account the payload of the lost voice
packets. The payload contains voice waveforms or audio data of the
corresponding time frame in the voice call. Rather, only the
information whether a voice packet is lost or received is taken
into account.
However, the content of lost voice packets may be very relevant for
reducing a prediction error of a voice quality estimate. For
example, a lost voice packet which carries audio data representing
the voice of a main presenter in the voice call may decrease the
perceived voice quality more than a lost voice packet which carries
audio data representing the silence of a listener in the voice
call. Consequently, by using the perceptual importance of the lost
voice packets for calculating a statistical metric which
subsequently can be used as input to a voice quality estimating
algorithm, the prediction error of a voice quality estimate can be
reduced.
By reducing the prediction error, problems that may result in one
or more participants perceiving degraded call quality can be
detected earlier and/or more accurately and thus better
handled.
According to example embodiments, the step of modifying the
statistical metric comprises weighting the lost voice packets
according to their perceptual importance. Consequently, the
perceptual importance of each lost voice packet may be taken into
account. E.g. two consecutive lost voice packets may have different
perceptual importance and thus weighted differently when
calculating the voice quality estimate. This may provide an
improved flexibility when modifying the statistical metrics.
According to example embodiments, the statistical metric relates to
groups of consecutive lost voice packets, each group comprising one
or more lost voice packets, wherein in the step of calculating the
statistical metrics, each group of consecutive lost voice packets
is weighted based on the number of consecutive lost voice packets
in the group, and wherein, in the step of modifying the statistical
metric, each group is further weighted based on the perceptual
importance of the lost voice packets in the group.
Since the statistical metric is based on groups of consecutive lost
voice packets, the pattern of the loss packets is taken into
account. It should be noted that a group may comprise just one lost
voice packets.
In a packet based voice communication system, random loss patterns
may decrease the voice quality less than if the lost packets are
grouped (e.g. a bursty loss pattern), since a larger number of
consecutive lost voice packets may increase the risk of
perceptually important data being lost. For example, loss of a
number of consecutive voice packets which carry audio data
representing the voice of a main presenter, while he or she is
making an important point, negatively affects a perceived voice
quality more than the loss of the same number of voice packets
spaced apart over a time period of the uplink data stream from the
main presenter. In other words, a bursty loss pattern may increase
the risk that a whole word or an important phoneme is lost, while a
more random loss pattern may be disregarded by a listener.
According to example embodiments, the perceptual importance for a
lost voice packet is estimated based on perceptual importance of
voice packets in the sequence of encoded voice packets which are
adjacent to the packets that were lost during the transmission from
the one or more end-points or discarded due to latency and/or
jitter of the transmission.
Consequently, the perceptual importance of a lost voice packet can
be estimated without having any information pertaining to the
actual lost voice packets. Moreover, since each voice packet
corresponds to a small time frame such as 1/100 or 1/50 second for
example, it is likely that a voice packet with a certain perceptual
importance is followed and preceded by voice packets with a similar
perceptual importance. It should be noted that a voice packet may
correspond to a first time frame (e.g. 20 ms) while another voice
packet in the same transmission may correspond to a second time
frame (e.g. 10 ms).
According to example embodiments, each voice packet in the received
sequence of encoded voice packets comprises a separate bit, or
separate bits, indicating the perceptual importance of the voice
packet. This may reduce the computational complexity for extracting
the perceptual importance from a voice packet, since no analyzing
of the actual voice waveforms in the voice packet needs to be
performed in order to extract the perceptual importance.
According to example embodiments, the method further comprises the
step of: receiving a signal indicating the perceptual importance of
each of the encoded voice packets transmitted from the one or more
end-points. This embodiment may be advantageous in that perceptual
importance of the lost packets are still described in the signal
indicating the perceptual importance of each of the encoded voice
packets transmitted from the one or more end-points. Consequently,
no analysis or calculation based on adjacent voice packets needs to
be performed in order to estimate the perceptual importance of the
lost voice packet(s). This may lead to a lower computational
complexity when modifying the statistical metric based on the
perceptual importance of the lost voice packets.
According to example embodiments, the method further comprises the
step of partially decoding at least some of the received encoded
voice packets in order to estimate perceptual importance of the
lost voice packets. The encoded voice packets may for example be
encoded using a modified discrete cosine transform, MDCT, based
encoder, wherein MDCT gain parameters are extracted by partially
decoding the at least some of the received encoded voice packets,
wherein the MDCT gain parameters are used for estimating the
perceptual importance of the lost voice packets. This may reduce
the computational complexity of the estimation of the perceptual
importance of the lost voice packets compared to a strategy where
the voice packets are fully decoded and analyzed.
According to example embodiments, the method further comprises the
step of fully decoding at least some of the received encoded voice
packets in order to estimate perceptual importance of the lost
voice packets. This may improve the estimation of the perceptual
importance of the lost packet and this in combination with packet
loss statistics on a packet level may reduce the prediction error
of the voice quality estimate compared to the strategy used in
ITU-T P.563, ANSI ANIQUE+.
According to example embodiments, the statistical metric includes
at least one of: a packet loss rate, PLR, which is the number of
lost voice packets in relation to a total number of transmitted
voice packets, and a burstiness factor, BF, which is one minus a
number of groups of consecutive lost voice packets in relation to
the number of lost voice packets.
These are typical parameters in conventional voice quality
estimating algorithms and by modifying at least one of these
statistical matrices, such voice quality estimating algorithms may
be reused.
According to example embodiments, the step of modifying the
statistical metric based on a perceptual importance of the lost
voice packets comprises linear or non-linear mapping of PLR and/or
BF. This will be explained in detail below.
According to example embodiments, the perceptual importance of a
voice packet is based on at least one of: a loudness value of the
voice packet, a phoneme category of the voice packet, and a
frequency band weighted signal energy level of the voice packet.
These parameters all address the perceptual importance of a voice
waveform and may be used separately or in combination in order to
extract the perceptual importance of a voice packet.
As used herein "loudness" represents a modeled psychoacoustic
measurement of sound intensity; in other words, loudness represents
an approximation of the volume of a sound or sounds as perceived by
the average user. The loudness may e.g. refer to a dialnorm value
(according to the ITU-R BS.1770 recommendations) of the voice
waveform. Other suitable loudness measurements standards may be
used such as Glasberg's and Moore's loudness model which provides
modifications and extensions to Zwicker's loudness model.
According to example embodiments, the received data further
comprises packets representing the one or more lost voice packets.
As explained above, a device in a teleconferencing system, e.g. a
mixer or a telephone endpoint usually comprises a Jitter buffer
which stores incoming voice packets, which may arrive in irregular
time intervals, to create voice packets in evenly spaced time
intervals. By also creating packets, for example with a mark for
lost voice packets, the output from the Jitter buffer always looks
the same when it comes to the number of voice packets per time
frame and the time period between the voice packets. This in turn
makes may reduce the complexity of the rest of the system, e.g. the
parts which calculate the voice quality estimate.
According to example embodiments there is provided a
computer-readable medium comprising computer code instructions
adapted to carry out any method of the first aspect when executed
on a device having processing capability.
According to example embodiments there is provided a device for
estimating a voice quality in a packet based voice communication
system. The device comprises a receiving stage configured to
receive data comprising a sequence of encoded voice packets
transmitted from one or more end-points in the voice communication
system, wherein encoded voice packets transmitted from the one or
more end-points comprises the received sequence of encoded voice
packets and one or more lost voice packets which were lost during
the transmission from the one or more end-points or discarded due
to latency and/or jitter in the transmission. The device further
comprises a calculating stage configured to calculate, based on the
received sequence of encoded voice packets, a statistical metric
relating to a number of lost voice packets. The device further
comprises a perceptual transformation stage configured to modify
the statistical metric based on a perceptual importance of the lost
voice packets, so as to reduce a prediction error of a voice
quality estimate when using the modified statistical metric as
input to a voice quality estimating algorithm.
II. Overview--Switchable Input Modes
According to a second aspect, example embodiments propose methods
for calculating a voice quality estimate in a packet based voice
communication system, devices implementing the methods, and
computer program product adapted to carry out the method. The
proposed methods, devices and computer program products may
generally have the same features and advantages. Generally,
features of the second aspect may have the same advantages as
corresponding features of the first aspect.
According to example embodiments there is provided a method for
calculating a voice quality estimate in a packet based voice
communication system. The method comprises the steps of: receiving
data comprising a sequence of encoded voice packets, using a voice
quality estimating algorithm to calculate the voice quality
estimate based on an input which is switchable between a first and
a second input mode.
In the first input mode, the input is a statistical metric relating
to the sequence of encoded voice packets.
In the second input mode, the input is a pre-processed version of
the statistical metric relating to the sequence of encoded voice
packets.
According to this method, the pre-processing improve the accuracy
of the voice quality estimate such that a prediction error of the
voice quality estimate based on the pre-processed version of the
statistical metric is reduced compared with the prediction error of
the voice quality estimate based on the statistical metric.
By providing two input modes, one which is for low complexity and
one that provides a higher accuracy, a more flexible method for
calculating voice quality estimate is provided. Moreover, since the
two input modes share the same voice quality estimation algorithm,
improved scalability may be achieved.
According to example embodiments, the method further comprises the
step of receiving input from one of the end-points indicating one
of the first and the second input mode to be selected. This input
may for example be triggered by that a user of an end-point
telephone perceives the voice quality to be unsatisfying. In this
case, a better estimate of the voice quality at e.g. the mixer may
be needed in order to better take care of the problem in the
transmission of the voice packet.
According to example embodiments, a selection between the first and
the second input mode is based on a computational load associated
with the first and the second input mode. In this case, for example
the mixer itself may switch from the second input mode to the first
input mode if the processors of the mixer are getting to
computationally overloaded. This may e.g. happen if many end-points
are connecting to the voice call, such that more mixing needs to be
performed.
According to example embodiments, the selection between the first
and the second input mode is based on the computational load
associated with the first and the second input mode in relation to
a desired voice quality estimation accuracy. Consequently the
switching between the two input modes may be a trade-off between
computational load of the device performing the method and the
accuracy of the voice quality estimate.
According to example embodiments, a selection between the first and
the second input mode is based on a preset mode.
According to example embodiments, the received data is transmitted
from one or more end-points in the voice communication system,
wherein encoded voice packets transmitted from the one or more
end-points comprises the received sequence of encoded voice packets
and one or more lost voice packets which were lost during the
transmission from the one or more end-points or discarded due to
latency and/or jitter in the transmission, wherein the statistical
metric is calculated from the received sequence of encoded voice
packets and relates to the lost voice packets, and wherein the
pre-processing relates to modification of the statistical metric
based on a perceptual importance of the lost voice packets. As
described above, by taking the perceptual importance of the lost
voice packets into account when calculating the voice quality
estimate, a more accurate voice quality estimate may be achieved.
It should be noted that any other type of pre-processing may be
employed, for example using Gaussian mixture models as described in
"An Improved GMM-Based Voice Quality Predictor" (Falk et. al), or
using articulatory transitions (i.e. active and passive
articulators) of vowel and consonant phonemes in order to modify
the statistical metric.
According to example embodiments, the pre-processing comprises
weighting the lost voice packets according to their perceptual
importance.
According to example embodiments, the statistical metric relates to
groups of consecutive lost voice packets, each group comprising one
or more lost packets, wherein the statistical metric is calculated
by weighting each group of consecutive lost voice packets based on
the number of consecutive lost voice packets in the group, and
wherein the pre-processing further comprises weighting each group
based on the perceptual importance of the lost voice packets in the
group.
According to example embodiments, the perceptual importance for a
lost voice packet is estimated based on perceptual importance of
voice packets in the sequence of encoded voice packets which are
adjacent to the packets that were lost during the transmission from
the one or more end-points.
According to example embodiments, the method further comprises the
step of at least partially decode at least some of the received
encoded voice packets in order to estimate perceptual importance of
the lost voice packets.
Such at least partially decoding may result in an increased
computational load on the device performing the method.
Consequently, when the second input mode comprises at least
partially decoding some of the received encoded voice packets; it
may be even more advantageous to have two input modes such that the
computational load of the device may be relaxed if needed.
According to example embodiment, each voice packet in the received
sequence of encoded voice packets comprises a separate bit
indicating the perceptual importance of the voice packet.
According to example embodiments, the method further comprises the
step of receiving a signal indicating the perceptual importance of
each of the encoded voice packets transmitted from the one or more
end-points.
According to example embodiment, the statistical metric includes at
least one of: a packet loss rate, PLR, which is the number of lost
voice packets in relation to a total number of transmitted voice
packets, and a burstiness factor, BF, which is one minus a number
of groups of consecutive lost voice packets in relation to the
number of lost voice packets.
According to example embodiment, the perceptual importance of a
voice packet is based on at least one of: a loudness value of the
voice packet, a phoneme category of the voice packet, and a
frequency band weighted signal energy level of the voice
packet.
According to example embodiment there is provided a
computer-readable medium comprising computer code instructions
adapted to carry out any method of the second aspect when executed
on a device having processing capability.
According to example embodiments there is provided a device for
calculating a voice quality estimate in a packet based voice
communication system. The device comprises a receiving stage
configured to receive data comprising a sequence of encoded voice
packets, and a voice quality estimation stage configured to use a
voice quality estimating algorithm to calculate the voice quality
estimate based on an input which is switchable between a first and
a second input mode, wherein, in the first input mode, the input is
a statistical metric relating to the sequence of encoded voice
packets, wherein in the second input mode, the input is a
pre-processed version of the statistical metric relating to the
sequence of encoded voice packets, and wherein a prediction error
of the voice quality estimate based on the pre-processed version of
the statistical metric is reduced compared with the prediction
error of the voice quality estimate based on the statistical
metric.
III. Example Embodiments
FIG. 1 describes a generalized block diagram of a voice quality
estimating device 100 in accordance with an example embodiment. The
device 100 is part of a packet based voice communication system,
e.g. a mixer or a telephone end-point in a teleconferencing
system.
The device 100 comprises two different parts 100a, 100b. The upper
part 100a in FIG. 1 comprising a Jitter buffer 102 and a stage 104
for decoding and packet loss concealment (PLC) is a typical voice
processing unit at a receiver, e.g. a mobile phone. The Jitter
buffer 102 typically is a buffer which receives incoming voice
packets 101 from other parts of the packet based voice
communication system. The incoming voice packets 101 usually arrive
in irregular time intervals due to problems with the uplink and/or
downlink data streams in the packet based voice communication
system. Some of the incoming packets are discarded since they are
late due to latency in the network, meaning that the corresponding
time segment of the voice call already has been rendered by a
speaker of the receiver. Some voice packets will be discarded due
to that the jitter characteristics exceed the capability of a
jitter buffer. The Jitter buffer 102 may output voice packets 103
in evenly spaced time intervals. Optionally the Jitter buffer 102
may also create packets representing to the lost voice packet, mark
them as such and include them in the outputted voice packets 103 in
evenly spaced time intervals. The mark for lost voice packet may be
a single bit in the outputted voice packets, e.g. a zero if the
voice packet is not lost and a one if the voice packet represents a
lost voice packet. The Jitter buffer may for example use sequence
numbers included in the voice packets in order to determine if
packets are lost or not and where those lost voice packets
originally (when transmitted) were located in the stream of voice
packets.
The stage 104 for decoding and PLC decodes the contents (payload)
of the stream of voice packets to synthesize voice waveforms. If
there are losses in voice packets, possibly marked by the Jitter
buffer 102 or otherwise known to the stage 104 (e.g. by a running
number in each voice packet), PLC is employed to estimate voice
waveforms of the lost packets by using the previously received
voice packets.
The lower part 100b of the device 100 in FIG. 1 is the part that
performs the estimation of the impact of lost packets on the
perceived voice quality, i.e. the part that calculates the voice
quality estimate 116. The calculated voice quality estimate 116 may
be outputted in the mean opinion score (MOS) scale.
The outputted voice packets 103 from the Jitter buffer 102 are
received by a packet loss statistics (PLS) calculating unit 106
(i.e. a calculation stage of the device 100). The PLS calculation
unit 106 comprises a receiving stage which is adapted to receive
data comprising a sequence of encoded voice packets 103 transmitted
from one or more end-points in the voice communication system. As
described above, some of the encoded voice packets transmitted from
the one or more end-points may have been lost during the
transmission from the one or more end-points to the device 100 or
discarded by the Jitter buffer 102 for being late. These lost
packets may cause a reduced perceptual quality of the voice call
which the encoded voice packets relate to.
The PLC calculation unit 106 is configured to calculate, based on
the received sequence 103 of encoded voice packets, a statistical
metric 107 relating to a number of lost voice packets. The
statistical metric 107 may include a packet loss rate, PLR, which
is the number of lost voice packets in relation to a total number
of transmitted voice packets. For example, if 10 out of 100 voice
packets are lost, the PLR equals 0.1.
Additionally or alternatively, the statistical metric 107 may
relate to a burstiness factor, BF, which is one minus a number of
groups of consecutive lost voice packets in relation to the number
of lost voice packets. If, out of the 10 lost voice packet, three
groups of consecutive lost packets can be formed, e.g. comprising
1, 3 and 6 lost voice packet each, the BF equals 1-(3/10)=0.7.
The device 100 comprises two different input modes 112, 114 for
calculating a voice quality estimate 116 in a voice quality
estimation stage 108. It should be noted that the voice quality
estimation stage 108 of the both modes are equal, i.e. the same
voice quality estimation algorithm is used independently of which
of the two input modes 112, 114 that is employed.
The first input mode 112 only uses statistics on a packet level,
e.g. the PLR and/or the BF, for calculating a voice quality
estimate. This is a typical way of calculating a voice quality
estimate which is computationally inexpensive but that may suffer
from low accuracy of the voice quality estimate since the actual
content of the lost voice packets is not considered.
An example embodiment of the voice quality estimation stage 108
will now be described. According to this embodiment, the voice
quality estimation stage 108 requires two inputs, which are a
packet loss rate value and a burstiness value.
The voice quality estimation stage 108 comprises L regression
models. L is a preset number of choices of burstiness factors. For
example, the voice quality estimation stage 108 may comprise six
regression models (L=6), each corresponding to a BF value of 0,
0.2, 0.4, 0.6, 0.8 and 1.0 respectively.
Given an input of a PLR value and BF value, two regression models
are selected that have the closest proximity to the value of BF,
and these two regression models estimate voice quality values from
the PLR value. The final voice quality is estimated by the weighted
sum of the two voice quality values.
However, in order to improve the accuracy of the voice quality
estimate 116, the second input mode 114 may be used. In the second
input mode 114, the input to the voice quality estimation stage 108
is a pre-processed version 111 of the statistical metric 107
relating to the sequence of encoded voice packets that is
calculated by the PLS calculation unit 100. The pre-processing made
in a pre-processing stage 110 of the device 100 which will be
described in detail below.
The switching between the first 112 and the second 114 input modes
may be based on a received input (not shown in FIG. 1) from one of
the end-points indicating one of the first and the second input
mode to be selected.
For example, telephone end-points connected to the packet based
teleconference may determine the operation mode of the device 100
(e.g. the conference server or another telephone end-point). Also
the conference server, or mixer, may send the appropriate signaling
for determining the operation mode, when the device 100 is a
telephone end-point connected to the packet based
teleconference.
According to other embodiments, the telephone end-point, or
conference server, where the voice quality estimate 116 is
calculated, can select between the first 112 and the second 114
input mode based on a computational load associated with the first
112 and the second 114 input mode. As understood from the above,
the calculation of the voice quality estimate 116 in the first
input mode 112 is fairly straight forward and thus has a low
computational complexity. The calculation of the voice quality
estimate 116 in the second input mode 114 often mean higher
computational complexity, depending on what type of pre-processing
that is employed. The device 100 calculating the voice quality
estimate 116 may thus determine which of the two input modes 112,
114 that should be used depending on its available computational
resources. Moreover, the selection between the first 112 and the
second 114 input mode may based on the computational load
associated with the first and the second input mode in relation to
a desired voice quality estimation accuracy.
The selection of the input mode to use can also be based on a
preset mode.
The pre-processing made in a pre-processing stage 110 of the device
100 may according to some embodiments relate to modifying the
statistical metric based on a perceptual importance of the lost
voice packets. For example, the PLR value and/or the BF value may
be transformed according to the perceptual importance of the lost
packets and further inputted to the voice quality estimation stage
108 which will use the inputted values 111 as explained above.
This use of the PLR and BF values in the voice quality estimation
stage 108, with or without being perceptually weighted will be
further explained in conjunction with FIG. 4 below.
The perceptual importance of a voice packet may be based on one or
more out of several properties of the voice waveform of the voice
packet. According to some embodiments, the perceptual importance is
based on a loudness value of the voice packet, i.e. a loudness
value of the voice waveform in the payload of the voice packet.
According to other embodiments, the perceptual importance is based
on a frequency band weighted signal energy level (or loudness
level) of the voice packet. This energy level may be transformed to
the loudness value (e.g. in the Sone unit) by:
Loudness=2.sup.0.1*P-4 (1)
where P is the frequency band-weighted signal energy level or
loudness level.
Other information, such as phoneme categories around or for the
voice packet can be used together with loudness information or
separately to calculate the perceptual importance of the voice
packet.
The calculation and use of the perceptual importance of the lost
voice packet will now be further described in conjunction with
FIGS. 2 and 3.
According to some embodiments, the statistical metric relates to
groups of consecutive lost voice packets, wherein in the step of
calculating the statistical metrics, each group of consecutive lost
voice packets is weighted based on the number of consecutive lost
voice packets in the group. This is described in FIG. 2. The upper
part of FIG. 2 exemplifies a relation between received packets 101
and lost packets 202 in encoded voice packets transmitted from the
one or more end-points to the device 100. As described above in
conjunction with FIG. 1, the Jitter buffer 102 may include packets
representing the one or more lost voice packets 202 in the data 103
received by the PLS calculation unit 106 and optionally by the
pre-processing stage 110. These packets may be empty and/or
comprising data indicating that they represent a lost packet. This
is described in FIG. 2 where the data 103 comprises the empty
packets representing the one or more lost voice packets 202.
Given a time series of lost voice packets that can be obtained from
103, a packet loss event function, s(k) can be defined, which
represents the number of consecutive lost packets at the k-th group
a loss event, for k=1, 2, . . . , K, where K is the number of
groups of consecutive lost packets. In FIG. 2, the number of such
groups is 3. The packet loss event function s(k) in this example is
a vector with the values [1, 3, 2]. Then the packet loss rate (PLR)
can be calculated by PLR=.SIGMA..sub.k=1.sup.Ks(k)/M (2)
where M is the total number of packets received 101 and packets
lost 202, i.e. M=14 in FIG. 2.
FIG. 2 further describe how the statistical metric 107 (in this
case the vector s(k)) is inputted to the pre-processing stage 110.
The pre-processing stage may for example be a perceptual
transformation stage configured to modify the statistical metric
107 based on a perceptual importance of the lost voice packets such
that each group is further weighted based on the perceptual
importance of the lost voice packets in the group. The perceptual
transformation stage transforms the vector s(k) to a new vector
z(k), from which a perceptual packet loss rate (pPLR) can be
calculated by pPLR=.SIGMA..sub.k=1.sup.Kz(k)/M (3)
z(k) is shown in FIG. 2 and this modified vector is used as the
basis for calculating the modified statistical metric pPLR
according to Equation 3.
The perceptual importance of the lost voice packets may be
calculated in a number of different ways. For example, the
pre-processing stage 110 may receive a signal 109 indicating the
perceptual importance of each of the encoded voice packets
transmitted from the one or more end-points. The signal 109 may
thus comprise the perceptual importance of all of the voice packets
in the data 103, including the lost voice packets 202. By employing
such additional signal 109, the computational load of the
pre-processing of the statistical metric 107 may be reduced.
According to other embodiments, the perceptual importance of a lost
voice packet is estimated based on perceptual importance of voice
packets in the sequence of encoded voice packets which are adjacent
to the packets that were lost during the transmission from the one
or more end-points or discarded by the Jitter buffer for being
late. This may be advantageous since no extra signal needs to be
transmitted, which may mean that the device 100 can be plugged into
a standard packet based communication system. According to some
embodiments, the device 100 may be configured to check if the
additional signal 109 is received and in that case use it, and if
the signal 109 is not received, estimate the perceptual importance
of a lost voice packet based on perceptual importance of voice
packets in the sequence of encoded voice packets which are adjacent
to the packets that were lost during the transmission from the one
or more end-points or discarded by the Jitter buffer for being
late.
FIG. 3 describe how the perceptual importance of a lost voice
packet is estimated based on perceptual importance of voice packets
in the sequence of encoded voice packets which are adjacent to the
packets that were lost during the transmission from the one or more
end-points or discarded by the Jitter buffer for being late.
Let X be a K-by-(d+1) feature matrix, in which the k-th row
represents the (d+1)-dimensional feature vector x(k) associated
with the k-th group of lost voice packets, d being the number of
features and the extra dimension is the constant shift term.
The feature vector x(k) contains useful information about the
speech characteristics or perceptual importance of the k-th group
of lost packets. In case the information is not available, i.e. no
signal 109 is received by the device 100; this information can be
estimated from the received neighboring packets around the lost
packets.
In one example embodiment, the feature vector can be based on PLR
and loudness information in the neighborhood of k-th group of lost
packets, expressed as x(k)=[PLR
L.sub.interp(k)L.sub.left(k)L.sub.right(k)L.sub.td(k)1] (4)
where
.times..function..function..function..function..function..times..times..t-
imes..times..times..function..function.<.times..times..function..functi-
on..function..function..function..function..function..function..function..-
function. ##EQU00001##
otherwise.
ST(k) and ED(k) are the time index for a packet right before and
after the k-th group loss.
L.sub.left(k)=.SIGMA..sub.i=0.sup.2L(ST(k)-i)/3, (7)
L.sub.right(k)=.SIGMA..sub.i=0.sup.2L(ED(k)+i)/3, (8)
L.sub.td(k)=[L(ED(k))-L(ST(k))]/[ED(k)-ST(k)] (9)
L(i) being the estimated loudness of the voice packet of the i-th
time index. This is summarized in FIG. 3.
The last term in x(k) is to accommodate the translation component
in the linear transformation described below.
The perceptual packet loss event function, z=[z(1) z(2) . . .
z(K)], (shown in FIG. 2) can be derived by z=Xws (10)
where w=[w(1) w(2) . . . w(d+1)] is the weighting factor of feature
matrix X and s=[s(1) s(2) . . . s(K)] is the vector representation
of packet loss event function (as described in equation 2 and FIG.
2). The weighting factor w can be trained using training data sets
such that the accuracy of the voice quality estimation is
improved.
The process of obtaining perceptual packet loss event function can
be interpreted as assigning perceptual importance to each group of
packet loss event, where the perceptual importance is estimated by
the linear combination of the features. It should be noted that a
nonlinear combination of the features may also be used. For
example, groups comprising over a threshold number of lost voice
packets may be squared in the s-vector.
The perceptual packet loss rate (pPLR) can then be defined as in
equation 3, pPLR=.SIGMA..sub.k=1.sup.Kz(k)/M, which thus transforms
the PLR to a perceptual domain for calculating the voice quality
estimate in more accurate manner.
This concept is depicted in FIG. 4. The regression curve 402 is
determined based on the BF value. The regression curve is based on
empirical data of actual perceived voice quality and is thus a
predefined reference. As described previously, the regression curve
may also be determined based on a BF value transformed to a
perceptual domain according to the above.
FIG. 4 shows how the use of the pPLR value 406 as input to the
voice quality estimation stage reduces the prediction error by a
large value (referred to as 408 in FIG. 4) compared to using the
conventional PLR value 404 as input. In other words, by applying
the process to transform PLR to pPLR, the data point 404 is
translated to the data point 406, resulting in reduced voice
quality estimation error using the same regression curve 402.
When deriving the perceptual importance of lost packets from
adjacent voice packets as described above, the perceptual
importance of the adjacent voice packet may be derived by partially
decoding the required voice packets out of the received encoded
voice packets. The payload of voice packets contains the encoded
bits to produce the transmitted voice waveforms when rendering the
voice call. The content of payload typically includes some form of
information, e.g. in a separate bit or bits, which can be utilized
to estimate energy level of the signal or loudness. In this case,
the loudness information can be estimated by partial decoding of
the payload instead of a full decoding process. For example, if the
encoded voice packets are encoded using a modified discrete cosine
transform, MDCT, based encoder, the MDCT gain parameters can be
extracted by partially decoding a received encoded voice packet.
The MDCT gain parameters can then be used for estimating the
perceptual importance of the voice packet (and any neighboring lost
voice packet).
For MDCT based coders, in order to reduce entropy in a subsequent
coding process, MDCT gain is firstly coded by an envelope coder
with logarithmic quantization. This gain is a direct reflection of
speech band signal energy level and can be retrieved by the device
that calculates the voice quality estimate. A frequency band
weighted signal energy level P can be calculated directly from MDCT
gain according to the following:
N: Number of bands in original band for loudness generation;
M: Number of bands in MDCT gain;
K: Number of bins in MDCT coefficients;
T: Number of frames in time axis;
Bin.sub.MDCT: MDCT bin coefficients which is a K*T matrix
Band.sub.Loudness=W.sub.1Bin.sub.MDCT.sup.2; (11)
where W.sub.1 is a N*K matrix to transform bin coefficients to band
energy. A band-weighted signal energy P can be calculated by:
P=BNF(W.sub.1Bin.sub.MDCT.sup.2); (12)
where B is a 1*N vector (Weighting of band perceptual importance
such as B-weighting), NF is a N*N matrix for normalization. The
MDCT gain is derived by: Band.sub.Gain=W.sub.2Bin.sub.MDCT,
(13)
where Bin.sub.MDCT is a K*T matrix, and W2 is an M*K matrix.
From equation 13 an inverse matrix can be approximated to recover
Bin.sub.MDCT' with identical band energy to Bin.sub.MDCT:
Bin.sub.MDCT'=W.sub.3Band.sub.Gain, (14)
where W.sub.3 is a K*M matrix.
By replacing Equation 14 into Equation 11 we can get:
P=BNFW.sub.1(W.sub.3Band.sub.Gain).sup.2=W.sub.4(W.sub.3Band.sub.Gain).su-
p.2 (15)
where W4 is a 1*K vector which is calculated by: W.sub.4=BNFW.sub.1
(16)
The above strategy for calculating the frequency band weighted
signal energy level P can be used for any transform based codec
(DCT, QMF etc) where the corresponding gain is extractable.
Other suitable methods for partially decoding at least some of the
received encoded voice packets in order to estimate perceptual
importance of the lost voice packets may equally well be used. One
such method is described in the US patent application US20090094026
(ALCATEL LUCENT USA INC).
It should be noted that according to some embodiments, the
perceptual importance of lost voice packets are estimated by fully
decoding at least some of the received encoded voice packets.
FIG. 5 describe a method 500 for modifying a statistical metric
relating to lost voice packets in a packet based voice
communication system. The first step S502 is the step of receiving
data comprising a sequence of encoded voice packets transmitted
from one or more end-points in the voice communication system,
wherein encoded voice packets transmitted from the one or more
end-points comprises the received sequence of encoded voice packets
and one or more lost voice packets which were lost during the
transmission from the one or more end-points or discarded due to
latency and/or jitter in the transmission. Based on the received
sequence of encoded voice packets, a statistical metric relating to
the lost voice packets is calculated S504. The statistical metric
is then modified S506 based on a perceptual importance of the lost
voice packets. Optionally, the modified statistical metric is used
as input to a voice quality estimating algorithm such that a
prediction error of a voice quality estimate is reduced compared to
using the unmodified statistical metric calculated in step S504 as
input to the same voice quality estimating algorithm.
FIG. 6 describe a method 600 for calculating a voice quality
estimate in a packet based voice communication system. The first
step S602 is the step of receiving data comprising a sequence of
encoded voice packets. Based on the received sequence of encoded
voice packets, a statistical metric is calculated S604. The final
step in the method 600 is the step of using a voice quality
estimating algorithm to calculate S608 the voice quality estimate.
The calculation S608 is based on an input which is switchable
between a first and a second input mode. In the first input mode,
the statistical metric calculated S604 based on the received
sequence of encoded voice packets is used as input. The second
input mode, the statistical metric calculated S604 based on the
received sequence of encoded voice packets is first pre-processed
S606 and then used as input for the calculation S608 of the voice
quality estimate. The step of pre-processing S606 leads to that a
prediction error of the voice quality estimate is reduced compared
to if no pre-processing is performed.
IV. Equivalents, Extensions, Alternatives and Miscellaneous
Further embodiments of the present disclosure will become apparent
to a person skilled in the art after studying the description
above. Even though the present description and drawings disclose
embodiments and examples, the disclosure is not restricted to these
specific examples. Numerous modifications and variations can be
made without departing from the scope of the present disclosure,
which is defined by the accompanying claims. Any reference signs
appearing in the claims are not to be understood as limiting their
scope.
Additionally, variations to the disclosed embodiments can be
understood and effected by the skilled person in practicing the
disclosure, from a study of the drawings, the disclosure, and the
appended claims. In the claims, the word "comprising" does not
exclude other elements or steps, and the indefinite article "a" or
"an" does not exclude a plurality. The mere fact that certain
measures are recited in mutually different dependent claims does
not indicate that a combination of these measured cannot be used to
advantage.
The systems and methods disclosed hereinabove may be implemented as
software, firmware, hardware or a combination thereof. In a
hardware implementation, the division of tasks between functional
units referred to in the above description does not necessarily
correspond to the division into physical units; to the contrary,
one physical component may have multiple functionalities, and one
task may be carried out by several physical components in
cooperation. Certain components or all components may be
implemented as software executed by a digital signal processor or
microprocessor, or be implemented as hardware or as an
application-specific integrated circuit. Such software may be
distributed on computer readable media, which may comprise computer
storage media (or non-transitory media) and communication media (or
transitory media). As is well known to a person skilled in the art,
the term computer storage media includes both volatile and
nonvolatile, removable and non-removable media implemented in any
method or technology for storage of information such as computer
readable instructions, data structures, program modules or other
data. Computer storage media includes, but is not limited to, RAM,
ROM, EEPROM, flash memory or other memory technology, CD-ROM,
digital versatile disks (DVD) or other optical disk storage,
magnetic cassettes, magnetic tape, magnetic disk storage or other
magnetic storage devices, or any other medium which can be used to
store the desired information and which can be accessed by a
computer. Further, it is well known to the skilled person that
communication media typically embodies computer readable
instructions, data structures, program modules or other data in a
modulated data signal such as a carrier wave or other transport
mechanism and includes any information delivery media.
* * * * *